Chapter 4 MAG catalogue
4.1 filter samples with high host data
sample_metadata <- sample_metadata%>%
filter(!sample %in% c("EHI02721", "EHI02712", "EHI02700", "EHI02720", "EHI02749", "EHI02719", "EHI02729", "EHI02715", "EHI02722"))
genome_counts_filt <- genome_counts %>%
select(one_of(c("genome",sample_metadata$sample)))%>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0))
genome_metadata <- genome_metadata %>%
semi_join(., genome_counts_filt, by = "genome") %>%
arrange(match(genome,genome_counts_filt$genome))
genome_tree <- keep.tip(genome_tree, tip=genome_metadata$genome) # keep only MAG tips
#load("data/genome_gifts.Rdata")4.2 Genome phylogeny
# Generate the phylum color heatmap
phylum_heatmap <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
arrange(match(genome, genome_tree$tip.label)) %>%
select(genome,phylum) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome")
# Generate basal tree
circular_tree <- force.ultrametric(genome_tree, method="extend") %>% # extend to ultrametric for the sake of visualisation
ggtree(., layout="fan", open.angle=10, size=0.5)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************# Add phylum ring
circular_tree <- gheatmap(circular_tree, phylum_heatmap, offset=0.55, width=0.1, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
geom_tiplab2(size=1, hjust=-0.1) +
theme(legend.position = "none", plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))
# Flush color scale to enable a new color scheme in the next ring
circular_tree <- circular_tree + new_scale_fill()
# Add completeness ring
circular_tree <- circular_tree +
new_scale_fill() +
scale_fill_gradient(low = "#d1f4ba", high = "#f4baba") +
geom_fruit(
data=genome_metadata,
geom=geom_bar,
mapping = aes(x=completeness, y=genome, fill=contamination),
offset = 0.55,
orientation="y",
stat="identity")
# Add genome-size ring
circular_tree <- circular_tree +
new_scale_fill() +
scale_fill_manual(values = "#cccccc") +
geom_fruit(
data=genome_metadata,
geom=geom_bar,
mapping = aes(x=length, y=genome),
offset = 0.05,
orientation="y",
stat="identity")
# Add text
circular_tree <- circular_tree +
annotate('text', x=3.6, y=0, label=' Phylum', family='arial', size=3.5) +
annotate('text', x=4.7, y=0, label=' Genome quality', family='arial', size=3.5) +
annotate('text', x=5.3, y=0, label=' Genome size', family='arial', size=3.5)
#Plot circular tree
circular_tree %>% open_tree(30) %>% rotate_tree(90)
4.3 Taxonomy overview
| phylum | mag_n |
|---|---|
| p__Bacillota_A | 526 |
| p__Bacteroidota | 127 |
| p__Bacillota | 53 |
| p__Pseudomonadota | 39 |
| p__Verrucomicrobiota | 35 |
| p__Synergistota | 18 |
| p__Actinomycetota | 15 |
| p__Patescibacteria | 13 |
| p__Desulfobacterota | 12 |
| p__Bacillota_C | 9 |
| p__Cyanobacteriota | 7 |
| p__Bacillota_B | 2 |
| p__Spirochaetota | 2 |
| p__Campylobacterota | 1 |
| p__Methanobacteriota | 1 |
4.4 Mag size (MB)
Mags average size (MB)
# A tibble: 1 × 1
Average_size
<dbl>
1 2102306.Minimum Mags size (MB)
# A tibble: 1 × 11
genome domain phylum class order family genus species completeness contamination length
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 EHA03306_bin.234 d__Bacteria p__Patescibacteria c__Saccharimonadia o__Saccharimonadales f__Nanos… g__N… s__ 52.6 7.13 326026Mags arrange by size (MB)
# A tibble: 860 × 11
genome domain phylum class order family genus species completeness contamination length
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 EHA03306_bin.234 d__Bacteria p__Patescibacteria c__Saccharimonadia o__Saccharimonadales f__Na… g__N… s__ 52.6 7.13 326026
2 EHA03306_bin.233 d__Bacteria p__Bacillota c__Bacilli o__RF39 f__UB… g__R… s__ 58.8 0.71 393300
3 EHA03305_bin.87 d__Bacteria p__Bacillota_A c__Clostridia o__Christensenellales f__CA… g__H… s__ 58.1 0.58 569748
4 EHA03337_bin.2 d__Bacteria p__Bacillota c__Bacilli o__RF39 f__UB… g__O… s__ 61.8 2.13 593604
5 EHA03305_bin.302 d__Bacteria p__Patescibacteria c__Saccharimonadia o__Saccharimonadales f__Na… g__N… s__ 55.2 4.48 600188
6 EHA03305_bin.141 d__Bacteria p__Patescibacteria c__Saccharimonadia o__Saccharimonadales f__Na… g__N… s__ 86.4 1.22 616303
7 EHA03306_bin.147 d__Bacteria p__Bacillota_A c__Clostridia o__TANB77 f__CA… g__C… s__ 52.8 6.44 621291
8 EHA03290_bin.28 d__Bacteria p__Patescibacteria c__Saccharimonadia o__Saccharimonadales f__Na… g__N… s__ 79.1 5.36 637327
9 EHA04588_bin.191 d__Bacteria p__Campylobacterota c__Campylobacteria o__Campylobacterales f__He… g__H… s__ 58.3 1.22 637744
10 EHA03337_bin.34 d__Bacteria p__Bacillota_C c__Negativicutes o__Selenomonadales f__Se… g__ s__ 51.6 0.1 652142
# ℹ 850 more rowsMags average size and completeness by phylum (MB)
genome_metadata %>%
group_by(phylum) %>%
summarise(average_size=mean(length),average_comp=mean(completeness))# A tibble: 15 × 3
phylum average_size average_comp
<chr> <dbl> <dbl>
1 p__Actinomycetota 1487860. 76.6
2 p__Bacillota 1297997. 83.9
3 p__Bacillota_A 2041433. 81.5
4 p__Bacillota_B 1738690 90
5 p__Bacillota_C 1510294. 90.9
6 p__Bacteroidota 3077607. 76.6
7 p__Campylobacterota 637744 58.3
8 p__Cyanobacteriota 1722146. 93.0
9 p__Desulfobacterota 1892601. 85.5
10 p__Methanobacteriota 1705853 99.8
11 p__Patescibacteria 763324. 77.4
12 p__Pseudomonadota 2121766. 82.7
13 p__Spirochaetota 1944581 94.7
14 p__Synergistota 1619963. 75.8
15 p__Verrucomicrobiota 2066309. 81.24.5 Genome quality
genome_metadata %>%
summarise(completeness_mean=mean(completeness) %>% round(2) %>% as.character(),
completeness_sd=sd(completeness) %>% round(2) %>% as.character(),
contamination_mean=mean(contamination) %>% round(2),
contamination_sd=sd(contamination) %>% round(2)) %>%
unite("Completeness",completeness_mean, completeness_sd, sep = " ± ", remove = TRUE) %>%
unite("Contamination",contamination_mean, contamination_sd, sep = " ± ", remove = TRUE) %>%
tt()| Completeness | Contamination |
|---|---|
| 80.99 ± 16.79 | 2.21 ± 2.32 |
#Generate quality biplot
genome_biplot <- genome_metadata %>%
select(c(genome,domain,phylum,completeness,contamination,length)) %>%
arrange(match(genome, rev(genome_tree$tip.label))) %>% #sort MAGs according to phylogenetic tree
ggplot(aes(x=completeness,y=contamination,size=length,color=phylum)) +
geom_point(alpha=0.7) +
ylim(c(10,0)) +
scale_color_manual(values=phylum_colors) +
labs(y= "Contamination", x = "Completeness") +
theme_classic() +
theme(legend.position = "none")
#Generate contamination boxplot
genome_contamination <- genome_metadata %>%
ggplot(aes(y=contamination)) +
ylim(c(10,0)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_void() +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.margin = unit(c(0, 0, 0.40, 0),"inches")) #add bottom-margin (top, right, bottom, left)
#Generate completeness boxplot
genome_completeness <- genome_metadata %>%
ggplot(aes(x=completeness)) +
xlim(c(50,100)) +
geom_boxplot(colour = "#999999", fill="#cccccc") +
theme_void() +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0.50),"inches")) #add left-margin (top, right, bottom, left)
#Render composite figure
grid.arrange(grobs = list(genome_completeness,genome_biplot,genome_contamination),
layout_matrix = rbind(c(1,1,1,1,1,1,1,1,1,1,1,4),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3),
c(2,2,2,2,2,2,2,2,2,2,2,3)))
4.6 Functional overview
# Aggregate basal GIFT into elements
genome_gifts<-genome_gifts_raw
function_table <- genome_gifts %>%
to.elements(., GIFT_db)
# Generate basal tree
function_tree <- force.ultrametric(genome_tree, method="extend") %>%
ggtree(., size = 0.3) ***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************#Add phylum colors next to the tree tips
function_tree <- gheatmap(function_tree, phylum_heatmap, offset=0, width=0.1, colnames=FALSE) +
scale_fill_manual(values=phylum_colors) +
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
#Add functions heatmap
function_tree <- gheatmap(function_tree, function_table, offset=0.5, width=3.5, colnames=FALSE) +
vexpand(.08) +
coord_cartesian(clip = "off") +
scale_fill_gradient(low = "#f4f4f4", high = "steelblue", na.value="white") +
labs(fill="GIFT")
#Reset fill scale to use a different colour profile in the heatmap
function_tree <- function_tree + new_scale_fill()
# Add completeness barplots
function_tree <- function_tree +
geom_fruit(data=genome_metadata,
geom=geom_bar,
grid.params=list(axis="x", text.size=2, nbreak = 1),
axis.params=list(vline=TRUE),
mapping = aes(x=length, y=genome, fill=completeness),
offset = 3.8,
orientation="y",
stat="identity") +
scale_fill_gradient(low = "#cf8888", high = "#a2cc87") +
labs(fill="Genome\ncompleteness")
function_tree
4.7 Functional ordination
# Generate the tSNE ordination
tSNE_function <- Rtsne(X=function_table, dims = 2, check_duplicates = FALSE)
# Plot the ordination
function_ordination <- tSNE_function$Y %>%
as.data.frame() %>%
mutate(genome=rownames(function_table)) %>%
inner_join(genome_metadata, by="genome") %>%
rename(tSNE1="V1", tSNE2="V2") %>%
select(genome,phylum,tSNE1,tSNE2, length) %>%
ggplot(aes(x = tSNE1, y = tSNE2, color = phylum, size=length))+
geom_point(shape=16, alpha=0.7) +
scale_color_manual(values=phylum_colors) +
theme_minimal() +
labs(color="Phylum", size="Genome size") +
guides(color = guide_legend(override.aes = list(size = 5))) # enlarge Phylum dots in legend
function_ordination